Sistem Pakar Diagnosis Masalah Kulit Yang Berbasis Web Dengan Metode Naive Bayes Classifier

Caesar Iskandar Mawikere
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Abstract

As we all know, the world of technology and information has become very advanced in recent years. This has led to numerous new ideas that make it easier for a person or organization to do their work and achieve their goals. Progress in technology and information greatly affects all areas, from culture and education to the economy, health, and even beauty. So, in this study, the researcher brought up a problem at a beauty clinic in Maguwoharjo, DIY, called Emila Aesthetic Solution. The problem is that consulting with patients is still done face-to-face. In other words, the clinic does not have a computerized system to help with consultations so that patients do not have to wait in line and can get treatment right away based on the results of their consultation. As a result, this study focused on developing an expert system to diagnose skin problems using the naïve Bayes classifier method.In this study, the system was made using the naïve Bayes classifier and the waterfall model for system development. The model was then coded into the system to be used. The goal of making this expert system was to make it easier for patients to get advice and for the clinic to handle patients.Based on the patient history data obtained, namely 20 patient data that experts have tested, this expert system, by implementing the naïve Bayes classifier method, yielded results indicating that the system's accuracy was 100 percent.
系统专家诊断基于Web的皮肤问题的方法Naive Bayes Classifier
众所周知,近年来,技术和信息的世界已经变得非常先进。这导致了许多新的想法,使个人或组织更容易完成他们的工作和实现他们的目标。技术和信息的进步极大地影响着各个领域,从文化、教育到经济、健康,甚至美容。因此,在这项研究中,研究人员在Maguwoharjo的一家美容诊所提出了一个问题,叫做Emila美学解决方案。问题是,与病人的咨询仍然是面对面的。换句话说,诊所没有计算机系统来帮助咨询,因此患者不必排队等候,可以根据咨询结果立即得到治疗。因此,本研究的重点是开发一个专家系统,使用naïve贝叶斯分类器方法来诊断皮肤问题。在本研究中,系统采用naïve贝叶斯分类器和瀑布模型进行系统开发。然后将该模型编码到要使用的系统中。制作这个专家系统的目的是让病人更容易获得建议,让诊所更容易处理病人。根据获得的患者病史数据,即专家测试的20例患者数据,该专家系统通过实现naïve贝叶斯分类器方法,得出的结果表明系统的准确率为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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